CMU VASC talk: Visual patterns with matching subband statistics and higher order image

Speaker: Joshua Gluckman

Abstract: Statistical representations of visual patterns are commonly used in computer vision. One such representation is a distribution measured from the output of a bank of filters (Gaussian, Laplacian, Gabor, wavelet etc). Both marginal and joint distributions of filter responses have been advocated and effectively used for a variety of vision tasks.

We begin by examining the ability of these representations to discriminate between an arbitrary pair of visual stimuli. Examples of patterns are derived that possess the same statistical properties, yet are "visually distinct." The existence of these patterns suggests the need for more powerful early visual representations.

It has been argued that the primary role of early vision is the modeling of statistical redundancy in natural imagery. One of the most striking properties of images is scale invariance. In the second part of this talk, this property is examined and a novel image representation, the higher order pyramid, is introduced. The representation is tuned to the scale invariant properties of images and constitutes a form of "higher order signal whitening."

BIO: Joshua Gluckman received the BS degree in economics from the University of Virginia (1992), the MS degree in computer science from the College of William and Mary (1995), and the PhD degree in computer science from Columbia University (2000). Since 2001, he has held the position of assistant professor of computer science at Polytechnic University in Brooklyn, NY. His area of research is computer vision.